whatmodel.sh / Haha-7B

Haha-7B

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Haha-7B is an open-weights language model from TeleAI (ZJared), released January 2025. Its published size is 7.62B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo ZJared/Haha-7B @22db1d1: licence tag apache-2.0. 7,615,616,512 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-01: month the maker's Hugging Face repo was created (createdAt 2025-01-04).

53.9±6.4#352 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Haha-7B, out of 1132 with a score.

#modelscorewhereRAMreleased
455REBEL-Llama-3-Armo-iter_1 Cornell AGI (REBEL)54±2local~18GB2024-08
456Apertus 70B Instruct (2509) Swiss AI54±6local~22GB2025-09
457Haha-7B TeleAI (ZJared)54±6local~18GB2025-01
458GPT-4 Turbo Preview (1106) OpenAI54±1comparison only2023-11
459Olmo 3 7B Think DPO Ai254±3local~6.2GB2025-11

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #245 of 334 * · 82% of its score · 1 test

Beats o3-mini, Llama 3.1 Nemotron Ultra 253B v1, Nova 2 Lite here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Mini, LFM2.5 1.2B Thinking here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL v357.3% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 69/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 82%blunt · at its level

$ where to run it

Other ways to run it

  1. Run it as published · needs ~15.23 GB · from huggingface.co/ZJared
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='ZJared/Haha-7B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/ZJared ↗

$ how the score was worked out · Berkeley Function Calling Lea… 82%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Berkeley Function Calling Leaderboard result archive1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 82%Apache-2.0
prior · pull toward the middle▇▇▇▇▇▇ 18%
weighted average= 53.9

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Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json